NDLI: Particle filter-based information acquisition for robust plan recognition

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Table of contents

Knowledge representation issues in perceptual reasoning managed situation assessment

Knowledge representation requirements for situation awareness

Situation assessment: procedural vs. logical

Tactical situation assessment challenges and implications for computational support

Situation assessment in urban combat environments

Representation and contribution-integration challenges in collaborative situation assessment

Human-aided multi-sensor fusion

DFIG Level 5 (User Refinement) issues supporting Situational Assessment Reasoning

A performance bound for maneuvering target tracking using best-fitting Gaussian distributions

DFIG level 5 (user refinement) issues supporting situational assessment reasoning

Manoeuvring target tracking with the IMM-VDA algorithm

VS-IMM using road map information for a ground target tracking

Pose angular-aiding for maneuvering target tracking

Multiple model tracker based on Gaussian mixture reduction for maneuvering targets in clutter

Non parametric target tracking in non uniform clutter

Smoothing framework for automatic track initiation in clutter

Set based tracking in clutter as a percolation process

Clutter map and target tracking

Distributed target tracking for nonlinear systems: application to bearings-only tracking

Optimal mixture approximation of the product of mixtures

The shifted Rayleigh filter for bearings only tracking

Tighter alternatives to the Cramer-Rao lower bound for discrete-time filtering

Mobility tracking in cellular networks with sequential Monte Carlo filters

Online multitarget detection and tracking using sequential Monte Carlo methods

Sequential clustering with particle filters-estimating the number of clusters from data

Sequential unscented Kalman filter for radar target tracking with range rate measurements

Multiple target tracking with possibly merged measurements modeled by point processes

Probability hypothesis density filter for multitarget multisensor tracking

Improved fast covariance intersection for distributed data fusion

Efficient particle filtering for road-constrained target tracking

A comparison of particle filters for recursive track-before-detect

A particle filter for joint detection and tracking of multiple objects in color video sequences

Particle filter-based information acquisition for robust plan recognition

CRLB with Pd<1 fused tracks

Re-entry vehicle tracking observability and theoretical bound

Limits of linear multitarget tracking

Relative error measures for evaluation of estimation algorithms

Differential Geometry Measures of Nonlinearity for Ground Moving Target Indicator (GMTI) Filtering

Analysis and integration of road projection methods for multiple ground target tracking

Nonlinear constrained tracking of targets on roads

Geo-tracking of a single constant-velocity target using broadband array sensor data from a maneuvering observer: a comparison of tracking techniques for nonlinear observations

A unifying framework for multi-target tracking and existence

Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches

Joint target tracking and identification. Part II. Shape video computing

Multiresolution EO/IR target tracking and identification

Over-the-horizon radar target tracking using MQP ionospheric modeling

The impact of tropospheric propagation on data fusion from multiple radars

Tracking ground targets using state vector fusion

Localization and fusion in multistage sonar

Interaction between estimator and estimation criteria

Comparison of forward Vs. feedback Kalman filter for aided inertial navigation system

Tracking multiple maneuvering targets from possibly unresolved, missing or false measurements

The behavior of model probability in multiple model algorithms

A Gaussian Mixture Filter for Near-Far Object Tracking

Sequential detection of target maneuvers

Optimal signal detection for false track discrimination

Mitigating the effects of residual biases with Schmidt-Kalman filtering

The modified Riccati equation in target tracking: some recent results

Multimodal image registration with applications to image fusion

Robust multi-sensor image registration by enhancing statistical correlation

Sensor quality evaluation in a multi-camera system

Sensor model appraisal for image registration

Hyperspectral image fusion using spectrally weighted kernels

How well do discrete Bayesian methods represent a true model for each class?

A comparison of ROC curves for label-fused within and across classifier systems

Classification fusion by decision templates for insulating part control

Fusion of two classifiers for speaker identification: removing and not removing silence

Pairwise classifier combination in the transferable belief model

Automated selection of fusion parameters through a segmentation of multi-sensor ROC curves

A multiple classifier approach for multisensor data fusion

Minimax robust image fusion using an estimation theory approach

Multi-frame image fusion using the expectation-maximization algorithm

A statistical multiscale approach to image segmentation and fusion

Characterisation of image fusion quality metrics for surveillance applications over bandlimited channels

Experimental tests of image fusion for night vision

Realtime image fusion and target learning & detection on a laptop attached processor

The Cramer-Rao bound applied to image fusion

Complex wavelet domain image fusion based on fractional lower order moments

Robust virtual forces-based camera positioning using a fusion of image content and intrinsic parameters

Mitigation of sensor and communication system impairments for multichannel image fusion and classification

Robust processing of SAR hologram data to mitigate impulse noise impairments

Tracking performance constrained MFR parameter control: applying constraints on prediction accuracy

Optimal adaptive waveform selection for target tracking

Energy efficient target tracking in a sensor network using non-myopic sensor scheduling

Farsighted sensor management strategies for move/stop tracking

Evaluating sensor allocations using equivalence classes of multi-target paths

An approximate dynamic programming approach to a communication constrained sensor management problem

Optimal observation trajectory of an active maneuvering radar

A risk-based object-oriented approach to sensor management

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

Multisource fusion for land cover classification using support vector machines

A new approach to image fusion based on cokriging

Adaptive band selection for hyperspectral image fusion using mutual information

Mining high-dimensional data for information fusion: a database-centric approach

Construction of a geospatial predictor by fusion of global and local models

Improving aerosol retrieval accuracy by integrating AERONET, MISR and MODIS data

Rule mining and classification in imperfect databases

Robust approaches for the data association problem

Clustered multidimensional data association for limited sensor resolutions

Performance analysis of optimal data association within a linear regression framework

Short-term ambiguity assessment to augment tracking data association information

A Bayesian fusion approach to change-points analysis of processes

Sequential auxiliary particle belief propagation

A fusion process based on belief theory for classification of facial basic emotions

Opportunistic information fusion: a new paradigm for next generation networked sensing systems

Efficient iterative importance sampling inference for dynamic Bayesian networks

Learning articulated motion structures with Bayesian networks

KNN particle filters for dynamic hybrid Bayesian networks

Adaptive human sensor model in sensor networks

Influence of timestamping error on data inaccuracy

Nonlinear blind sensor fusion and identification

Application of fuzzy preference modelling to the fusion of sensory profile data

Bridging merging prioritized information in possibilistic logic and inconsistency handling methods

Ontology-driven information integration for operational decision support

Ontology meta-model for building a situational picture of catastrophic events

The semantic challenge for situation assessments

Grammatical methods for situation and threat analysis

A comparative study on model selection and multiple model fusion

A Bayesian approach to extended object tracking and tracking of loosely structured target groups

Relation of optimal local compression and local likelihood ratio

A comparative study on the use of road network information in GMTI tracking

Sixth International Conference of Information Fusion, 2003. Proceedings of the

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

Proceedings of the Third International Conference on Information Fusion

Particle filter-based information acquisition for robust plan recognition

Content Provider	IEEE Xplore Digital Library
Author	Ronnie, L. Johansson, M. Suzic, R.
Copyright Year	2005
Description	Author affiliation: Centre for Autonomous Syst., KTH Nada, Stockholm, Sweden (Ronnie, L.; Johansson, M.)
Abstract	Plan recognition generates high-level information of opponents' plans, typically a probability distribution over a set of plausible plans. Estimations of plans are in our work, made at different decision-levels, both company-level and the subsumed platoon-level. Naturally, successful plan recognition is heavily dependent on the data that is supplied, and, hence, sensor management is a necessity. A key feature of the sensor management discussed here is that it is driven by the information need of the plan recognition process. In our research, we have presented a general framework for connecting information need to sensor management. In our framework implementation, an essential part is the prioritization of sensing tasks, which is necessary to efficiently utilize limited sensing resources. In our first implementation, the priorities were calculated from, for instance, the estimated threats of opponents (as a function of plan estimates), the distance to the opponent, and the uncertainty in its position. In this article, we add a particle filter method to more carefully represent the uncertainty in the opponent state estimate to make prioritization more well founded and, ultimately, to achieve robust plan recognition. By using the particle filter, we can obtain more reliable state estimate (through the particle filter's ability to represent complex probability distributions) and also a statistically based threat variation (through Monte-Carlo simulation). The state transition model of the particle filter can also be used to predict future states to direct sensors with a time delay (a common property of large-scale sensing systems), such as sensors mounted on UAVs that have to travel some distance to make a measurement.
File Size	3885073
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1591853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information filtering Information filters Robustness Particle filters Probability distribution Uncertainty State estimation Sensor systems Joining processes Predictive models
Content Type	Text
Resource Type	Article

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